MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer

Abstract

ObjectiveTo reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).MethodsMRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).ResultsThe positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).ConclusionsWe found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Transverse prostate maximum sectional area can predict clinically significant prostate cancer in PI-RADS 3 lesions at multiparametric magnetic resonance imaging

“…These results were in line with those reported by Jiang S et al. ( 11 ) who first proposed the introduction of these new tools. As suggested by the authors, the reason was possibly that the prostate is not a regular geometric solid and, therefore, any distortion, especially due to the growth mode of the tumor, could affect the calculation of the prostate volume.…”

“…Subsequently, the following parameters were measured on axial FR-FSE T2w sequences as previously described ( 11 ): 1) TransPA, 2) TransCGA, 3) TransPZA, and 4) TransPAI ( Figure 2 ).…”

“…A recent study by Jiang S et al. ( 11 ) investigated new tools for predicting PCa in a cohort of patients who underwent mpMRI and biopsy, such as maximum sectional areas on transverse, coronal and sagittal T2-weighted (T2w) images of the entire prostate, and the peripheral and transition zones (PZ and TZ), separately. They found that prostate maximum sectional area (PA) is a good prostatic imaging parameter for predicting PCa, and the transverse sectional area of the central gland is significantly smaller in patients with PCa (p < 0.001) and has the greatest significant area under the receiver operating characteristic curve (AUC) (0.801) of all the predictors.…”

A SelectMDx/magnetic resonance imaging‐based nomogram to diagnose prostate cancer

“… 7 , 10 Studies have shown the significance of PI‐RADS scores of 4 and 5 for the detection of PCa. 15 , 16 , 17 , 18 , 19 Our study also confirms these findings.…”

“…Additionally, multiple studies have shown a role for mpMRI in the detection of PCa rendering it the imaging modality of choice 7,10 . Studies have shown the significance of PI‐RADS scores of 4 and 5 for the detection of PCa 15–19 . Our study also confirms these findings.…”